Knowledge catalogue
arXiv:2604.10252v1 Announce Type: new Abstract: Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the mod
arXiv:2604.11064v1 Announce Type: cross Abstract: Continual Learning (CL) aims to train neural networks on a dynamic stream of tasks without forgetting previously learned knowledge. Among optimization
arXiv:2604.11146v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, thereby preserving privacy. However, F
arXiv:2308.01917v3 Announce Type: replace-cross Abstract: Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasio
arXiv:2601.16399v5 Announce Type: replace Abstract: We study a structured bi-level optimization problem where the upper-level objective is a smooth function and the lower-level problem is policy optim
arXiv:2604.09932v1 Announce Type: cross Abstract: Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial conditio
arXiv:2604.09752v1 Announce Type: cross Abstract: During the deployment of Large Language Models (LLMs), the autoregressive decoding phase on heterogeneous NPU platforms (e.g., Ascend 910B) faces seve
arXiv:2604.11709v1 Announce Type: new Abstract: Accurate and rapid structural damage assessment (SDA) is crucial for post-disaster management, helping responders prioritise resources, plan rescues, an
arXiv:2510.03989v2 Announce Type: replace-cross Abstract: The Transformer architecture has revolutionized the field of sequence modeling and underpins the recent breakthroughs in large language models
arXiv:2604.05142v2 Announce Type: replace Abstract: As artificial intelligence systems (AIs) become increasingly produced by recursive self-improvement, a form of evolution may emerge, with the traits
arXiv:2604.11791v1 Announce Type: cross Abstract: Reasoning has become a central capability in large language models. Recent research has shown that reasoning performance can be improved by looping an
arXiv:2511.05476v4 Announce Type: replace-cross Abstract: Transformer-based language models of code have achieved state-of-the-art performance across a wide range of software analytics tasks, but thei
arXiv:2604.10356v1 Announce Type: cross Abstract: We present a minimal mathematical model for conducting patterns that separates geometric trajectory from temporal parametrization. The model is based
arXiv:2604.09979v1 Announce Type: cross Abstract: Self-supervised representation learning is central to modern machine learning because it extracts structured latent features from unlabeled data and e
arXiv:2604.09685v1 Announce Type: new Abstract: We describe a zero-shot pipeline developed for the ACCIDENT @ CVPR 2026 challenge. The challenge requires predicting when, where, and what type of traff
arXiv:2604.10177v1 Announce Type: cross Abstract: We study the piecewise-stationary restless multi-armed bandit (PS-RMAB) problem, where each arm evolves as a Markov chain but mean rewards may change
arXiv:2604.10957v1 Announce Type: cross Abstract: Writing systems are cultural replicators whose evolution has never been studied quantitatively at global scale. We compile the Global Script Database
arXiv:2604.10248v1 Announce Type: new Abstract: Complex systems such as aircraft engines, turbines, and industrial machinery often operate under dynamically changing conditions. These varying operatin
arXiv:2407.10953v5 Announce Type: replace Abstract: The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improv
arXiv:2412.20495v2 Announce Type: replace-cross Abstract: The proliferation of real-world health data enables multi-institutional survival studies, yet privacy constraints preclude centralizing sensit
arXiv:2604.10506v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have made significant strides in static image understanding but continue to face critical hurdles in spatiotemporal reason
arXiv:2604.11125v1 Announce Type: new Abstract: India generates vast biomedical data through postgraduate research, government hospital services and audits, government schemes, private hospitals and t
arXiv:2604.10873v1 Announce Type: new Abstract: We propose an operational, quantitative definition of intelligence for arbitrary physical systems. The intelligence density of a system is the ratio of
arXiv:2604.10427v1 Announce Type: cross Abstract: We develop a queueing-theoretic framework to model the temporal evolution of cyber-attack surfaces, where the number of active vulnerabilities is repr
arXiv:2604.10058v1 Announce Type: new Abstract: In this paper, we discuss an efficient algorithm for computing the growth distance between two compact convex sets with representable support functions.
arXiv:2507.02098v2 Announce Type: replace-cross Abstract: In this paper, we present a robust and adaptive model predictive control (MPC) framework for uncertain nonlinear systems affected by bounded d
arXiv:2604.10368v1 Announce Type: new Abstract: Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures
arXiv:2510.10182v2 Announce Type: replace-cross Abstract: Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the fundamen
arXiv:2508.09977v4 Announce Type: replace Abstract: In the context of novel view synthesis, 3D Gaussian Splatting (3DGS) has recently emerged as an efficient and competitive counterpart to Neural Radi
arXiv:2309.17257v2 Announce Type: replace Abstract: The ability to anticipate possible future human actions is essential for a wide range of applications, including autonomous driving and human-robot
arXiv:2604.11048v1 Announce Type: cross Abstract: Imbuing Large Language Models (LLMs) with specific personas is prevalent for tailoring interaction styles, yet the impact on underlying cognitive capa
arXiv:2604.09921v1 Announce Type: new Abstract: Much work has been done on designing fast and accurate sampling for diffusion language models (dLLMs). However, these efforts have largely focused on th
arXiv:2604.10149v1 Announce Type: cross Abstract: Graph attention networks (GATs) provide one of the best frameworks for learning node representations in relational data; but, existing variants such a
arXiv:2604.11582v1 Announce Type: cross Abstract: Standard subword tokenization methods fragment numbers inconsistently, causing large language models (LLMs) to lose positional and decimal structure -
arXiv:2603.12221v2 Announce Type: replace Abstract: This paper addresses the expression (EXPR) recognition challenge in the 10th Affective Behavior Analysis in-the-Wild (ABAW) workshop and competition
arXiv:2512.05534v4 Announce Type: replace-cross Abstract: As AI models achieve remarkable capabilities across diverse domains, understanding what representations they learn and how they encode concept
arXiv:2511.06609v3 Announce Type: replace Abstract: The accurate forecasting of complex, high-dimensional dynamical systems from observational data is a fundamental task across numerous scientific and
arXiv:2604.10210v1 Announce Type: new Abstract: Learning multi-scale representations is the common strategy to tackle object scale variation in dense prediction tasks. Although existing feature pyrami
arXiv:2604.11272v1 Announce Type: cross Abstract: Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design, yet remains constrained by severe label sparsity and th
arXiv:2604.10096v1 Announce Type: new Abstract: Current embodied intelligent systems still face a substantial gap between high-level reasoning and low-level physical execution in open-world environmen
arXiv:2507.12475v2 Announce Type: replace-cross Abstract: The St. Petersburg paradox presents a longstanding challenge in decision theory: its classical expected value diverges, yet no correspondingly
arXiv:2604.10303v1 Announce Type: new Abstract: High-quality Late Gadolinium Enhancement (LGE) MRI can be helpful for atrial fibrillation management, yet scan quality is frequently compromised by pati
arXiv:2604.10157v1 Announce Type: cross Abstract: Band gap engineering of oxide semiconductors through doping is critical for photocatalysis and optoelectronics, yet the combinatorial space of dopant
arXiv:2604.09819v1 Announce Type: cross Abstract: We introduce ACCIDENT, a benchmark dataset for traffic accident detection in CCTV footage, designed to evaluate models in supervised (IID and OOD) and
arXiv:2503.20654v4 Announce Type: replace-cross Abstract: Collecting real-world vehicle accident videos for autonomous driving research is challenging due to their rarity and complexity. While existin
arXiv:2604.09564v1 Announce Type: cross Abstract: We present ACE-Bench (Azure SDK Coding Evaluation Benchmark), an execution-free benchmark that provides fast, reproducible pass or fail signals for wh
arXiv:2604.09572v1 Announce Type: cross Abstract: We introduce ACE-TA, the Agentic Coding and Explanations Teaching Assistant framework, that autonomously routes conceptual queries drawn from programm
arXiv:2511.16164v2 Announce Type: replace Abstract: In a growing renewable based energy system, accurate and reliable wind power forecasts are crucial for grid stability, balancing supply and demand a
arXiv:2604.11708v1 Announce Type: new Abstract: Open-source software for cyber-physical systems (CPS) often lacks robust testing involving robotic platforms, resulting in critical errors that remain u
arXiv:2511.18082v3 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remain
arXiv:2604.11410v1 Announce Type: new Abstract: We present a framework for bridging the gap between sensor attack detection and recovery in cyber-physical systems. The proposed framework models modern
arXiv:2604.10084v1 Announce Type: new Abstract: Objective: The study aims to address the challenge of aligning Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which is difficu
arXiv:2604.09673v1 Announce Type: cross Abstract: The mirror self-recognition test evaluates whether a subject touches a mark on its own body that is visible only in a mirror, and is widely used as an
arXiv:2604.11103v1 Announce Type: cross Abstract: Role-playing has garnered rising attention as it provides a strong foundation for human-machine interaction and facilitates sociological research. How
arXiv:2604.09747v1 Announce Type: cross Abstract: Large Language Model (LLM) agents have achieved rapid adoption and demonstrated remarkable capabilities across a wide range of applications. To improv
arXiv:2604.10233v1 Announce Type: cross Abstract: 3D medical image analysis is of great importance in disease diagnosis and treatment. Recently, multimodal large language models (MLLMs) have exhibited
arXiv:2505.02796v2 Announce Type: replace-cross Abstract: We study how a budget-constrained bidder should learn to adaptively bid in repeated first-price auctions to maximize her cumulative payoff. Th
arXiv:2604.10607v1 Announce Type: cross Abstract: H-EFT-VA established a physics-informed solution to the Barren Plateau (BP) problem via a hierarchical EFT UV-cutoff, guaranteeing gradient variance i
arXiv:2604.10335v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong performance in math reasoning benchmarks, but their performance varies inconsistently across problems wi
arXiv:2604.09617v1 Announce Type: new Abstract: Transparent and standardized documentation is essential for building trustworthy generative AI (GAI) systems. However, existing automated methods for ge